




"""simple docstring"""





import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


A				=							16
A				=							32

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase = 16					,							UpperCamelCase = "bert-base-cased"				)      ->      List[Any]:


			"""simple docstring"""
			__UpperCAmelCase						:			Optional[Any]												=       AutoTokenizer.from_pretrained(UpperCamelCase				)
			__UpperCAmelCase						:			Optional[Any]												=       load_dataset("glue"					,							"mrpc"				)

			def tokenize_function(UpperCamelCase				):
						# max_length=None => use the model max length (it's actually the default)
						__UpperCAmelCase						:			List[Any]												=       tokenizer(examples["sentence1"]					,							examples["sentence2"]					,							truncation=UpperCamelCase					,							max_length=UpperCamelCase				)
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			__UpperCAmelCase						:			Optional[int]												=       datasets.map(
			    UpperCamelCase					,							batched=UpperCamelCase					,							remove_columns=["idx", "sentence1", "sentence2"]					,							load_from_cache_file=UpperCamelCase				)

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			__UpperCAmelCase						:			List[Any]												=       tokenized_datasets.rename_column("label"					,							"labels"				)

			def collate_fn(UpperCamelCase				):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(UpperCamelCase					,							padding="max_length"					,							max_length=128					,							return_tensors="pt"				)
						return tokenizer.pad(UpperCamelCase					,							padding="longest"					,							return_tensors="pt"				)

			# Instantiate dataloaders.
			__UpperCAmelCase						:			List[Any]												=       DataLoader(
			    tokenized_datasets["train"]					,							shuffle=UpperCamelCase					,							collate_fn=UpperCamelCase					,							batch_size=UpperCamelCase				)
			__UpperCAmelCase						:			Any												=       DataLoader(
			    tokenized_datasets["validation"]					,							shuffle=UpperCamelCase					,							collate_fn=UpperCamelCase					,							batch_size=UpperCamelCase				)

			return train_dataloader, eval_dataloader

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase				)      ->      List[str]:


			"""simple docstring"""
			__UpperCAmelCase						:			Dict												=       Accelerator()

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			__UpperCAmelCase						:			Any												=       config["lr"]
			__UpperCAmelCase						:			str												=       int(config["num_epochs"]				)
			__UpperCAmelCase						:			Tuple												=       int(config["seed"]				)
			__UpperCAmelCase						:			List[Any]												=       int(config["batch_size"]				)
			__UpperCAmelCase						:			Union[str, Any]												=       args.model_name_or_path

			set_seed(UpperCamelCase				)
			__UpperCAmelCase						,  __UpperCAmelCase						:			Optional[Any]												=       get_dataloaders(UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			__UpperCAmelCase						:			Dict												=       AutoModelForSequenceClassification.from_pretrained(UpperCamelCase					,							return_dict=UpperCamelCase				)

			# Instantiate optimizer
			__UpperCAmelCase						:			List[str]												=       (
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			__UpperCAmelCase						:			Optional[Any]												=       optimizer_cls(params=model.parameters()					,							lr=UpperCamelCase				)

			if accelerator.state.deepspeed_plugin is not None:
						__UpperCAmelCase						:			Tuple												=       accelerator.state.deepspeed_plugin.deepspeed_config[
						    "gradient_accumulation_steps"
						]
			else:
						__UpperCAmelCase						:			Union[str, Any]												=       1
			__UpperCAmelCase						:			Any												=       (len(UpperCamelCase				) * num_epochs) // gradient_accumulation_steps

			# Instantiate scheduler
			if (
			    accelerator.state.deepspeed_plugin is None
			    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
			):
						__UpperCAmelCase						:			Tuple												=       get_linear_schedule_with_warmup(
						    optimizer=UpperCamelCase					,							num_warmup_steps=0					,							num_training_steps=UpperCamelCase					,							)
			else:
						__UpperCAmelCase						:			str												=       DummyScheduler(UpperCamelCase					,							total_num_steps=UpperCamelCase					,							warmup_num_steps=0				)

			# Prepare everything
			# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
			# prepare method.
			__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						:			int												=       accelerator.prepare(
			    UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)

			# We need to keep track of how many total steps we have iterated over
			__UpperCAmelCase						:			Union[str, Any]												=       0
			# We also need to keep track of the stating epoch so files are named properly
			__UpperCAmelCase						:			Union[str, Any]												=       0

			# Now we train the model
			__UpperCAmelCase						:			str												=       evaluate.load("glue"					,							"mrpc"				)
			__UpperCAmelCase						:			Dict												=       0
			__UpperCAmelCase						:			int												=       {}
			for epoch in range(UpperCamelCase					,							UpperCamelCase				):
						model.train()
						for step, batch in enumerate(UpperCamelCase				):
									__UpperCAmelCase						:			Union[str, Any]												=       model(**UpperCamelCase				)
									__UpperCAmelCase						:			Tuple												=       outputs.loss
									__UpperCAmelCase						:			Dict												=       loss / gradient_accumulation_steps
									accelerator.backward(UpperCamelCase				)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									overall_step += 1

						model.eval()
						__UpperCAmelCase						:			str												=       0
						for step, batch in enumerate(UpperCamelCase				):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device				)
									with torch.no_grad():
												__UpperCAmelCase						:			str												=       model(**UpperCamelCase				)
									__UpperCAmelCase						:			str												=       outputs.logits.argmax(dim=-1				)
									# It is slightly faster to call this once, than multiple times
									__UpperCAmelCase						,  __UpperCAmelCase						:			str												=       accelerator.gather(
									    (predictions, batch["labels"])				)  # If we are in a multiprocess environment, the last batch has duplicates
									if accelerator.use_distributed:
												if step == len(UpperCamelCase				) - 1:
															__UpperCAmelCase						:			List[str]												=       predictions[: len(eval_dataloader.dataset				) - samples_seen]
															__UpperCAmelCase						:			Dict												=       references[: len(eval_dataloader.dataset				) - samples_seen]
												else:
															samples_seen += references.shape[0]
									metric.add_batch(
									    predictions=UpperCamelCase					,							references=UpperCamelCase					,							)

						__UpperCAmelCase						:			Dict												=       metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f"epoch {epoch}:"					,							UpperCamelCase				)
						__UpperCAmelCase						:			Optional[Any]												=       eval_metric["accuracy"]

						if best_performance < eval_metric["accuracy"]:
									__UpperCAmelCase						:			List[Any]												=       eval_metric["accuracy"]

			if args.performance_lower_bound is not None:
						assert (
						    args.performance_lower_bound <= best_performance
						), f"Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"

			accelerator.wait_for_everyone()
			if accelerator.is_main_process:
						with open(os.path.join(args.output_dir					,							"all_results.json"				)					,							"w"				) as f:
									json.dump(UpperCamelCase					,							UpperCamelCase				)

def        _UpperCamelCase     (      )      ->      Dict:


			"""simple docstring"""
			__UpperCAmelCase						:			int												=       argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."				)
			parser.add_argument(
			    "--model_name_or_path"					,							type=UpperCamelCase					,							default="bert-base-cased"					,							help="Path to pretrained model or model identifier from huggingface.co/models."					,							required=UpperCamelCase					,							)
			parser.add_argument(
			    "--output_dir"					,							type=UpperCamelCase					,							default="."					,							help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."					,							)
			parser.add_argument(
			    "--performance_lower_bound"					,							type=UpperCamelCase					,							default=UpperCamelCase					,							help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."					,							)
			parser.add_argument(
			    "--num_epochs"					,							type=UpperCamelCase					,							default=3					,							help="Number of train epochs."					,							)
			__UpperCAmelCase						:			str												=       parser.parse_args()
			__UpperCAmelCase						:			List[Any]												=       {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
			training_function(UpperCamelCase					,							UpperCamelCase				)


if __name__ == "__main__":
					main()








'''simple docstring'''







import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_     						=				logging.get_logger(__name__)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase=False						):






   '''simple docstring'''







   A__       =     []
   for i in range(config.num_hidden_layers						):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""deit.encoder.layer.{i}.layernorm_before.weight""")						)
      rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""deit.encoder.layer.{i}.layernorm_before.bias""")						)
      rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""deit.encoder.layer.{i}.attention.output.dense.weight""")						)
      rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""deit.encoder.layer.{i}.attention.output.dense.bias""")						)
      rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""deit.encoder.layer.{i}.layernorm_after.weight""")						)
      rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""deit.encoder.layer.{i}.layernorm_after.bias""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""deit.encoder.layer.{i}.intermediate.dense.weight""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""deit.encoder.layer.{i}.intermediate.dense.bias""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""deit.encoder.layer.{i}.output.dense.weight""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""deit.encoder.layer.{i}.output.dense.bias""")						)

   # projection layer + position embeddings
   rename_keys.extend(
       [
           ('cls_token', 'deit.embeddings.cls_token'),
           ('dist_token', 'deit.embeddings.distillation_token'),
           ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
           ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
           ('pos_embed', 'deit.embeddings.position_embeddings'),
       ]						)

   if base_model:
      # layernorm + pooler
      rename_keys.extend(
          [
              ('norm.weight', 'layernorm.weight'),
              ('norm.bias', 'layernorm.bias'),
              ('pre_logits.fc.weight', 'pooler.dense.weight'),
              ('pre_logits.fc.bias', 'pooler.dense.bias'),
          ]						)

      # if just the base model, we should remove "deit" from all keys that start with "deit"
      A__       =     [(pair[0], pair[1][4:]) if pair[1].startswith('deit'						) else pair for pair in rename_keys]
   else:
      # layernorm + classification heads
      rename_keys.extend(
          [
              ('norm.weight', 'deit.layernorm.weight'),
              ('norm.bias', 'deit.layernorm.bias'),
              ('head.weight', 'cls_classifier.weight'),
              ('head.bias', 'cls_classifier.bias'),
              ('head_dist.weight', 'distillation_classifier.weight'),
              ('head_dist.bias', 'distillation_classifier.bias'),
          ]						)

   return rename_keys







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=False						):






   '''simple docstring'''







   for i in range(config.num_hidden_layers						):
      if base_model:
         A__       =     ''
      else:
         A__       =     'deit.'
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      A__       =     state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""						)
      A__       =     state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""						)
      # next, add query, keys and values (in that order) to the state dict
      A__       =     in_proj_weight[
          : config.hidden_size, :
      ]
      A__       =     in_proj_bias[: config.hidden_size]
      A__       =     in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      A__       =     in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      A__       =     in_proj_weight[
          -config.hidden_size :, :
      ]
      A__       =     in_proj_bias[-config.hidden_size :]







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     dct.pop(UpperCAmelCase						)
   A__       =     val







def 						_A      ( ):






   '''simple docstring'''







   A__       =     'http://images.cocodataset.org/val2017/000000039769.jpg'
   A__       =     Image.open(requests.get(UpperCAmelCase      ,stream=UpperCAmelCase						).raw						)
   return im







@torch.no_grad()
def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     DeiTConfig()
   # all deit models have fine-tuned heads
   A__       =     False
   # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
   A__       =     1000
   A__       =     'huggingface/label-files'
   A__       =     'imagenet-1k-id2label.json'
   A__       =     json.load(open(hf_hub_download(UpperCAmelCase      ,UpperCAmelCase      ,repo_type='dataset'						)      ,'r'						)						)
   A__       =     {int(UpperCAmelCase						): v for k, v in idalabel.items()}
   A__       =     idalabel
   A__       =     {v: k for k, v in idalabel.items()}
   A__       =     int(deit_name[-6:-4]						)
   A__       =     int(deit_name[-3:]						)
   # size of the architecture
   if deit_name[9:].startswith('tiny'						):
      A__       =     192
      A__       =     768
      A__       =     12
      A__       =     3
   elif deit_name[9:].startswith('small'						):
      A__       =     384
      A__       =     1536
      A__       =     12
      A__       =     6
   if deit_name[9:].startswith('base'						):
      pass
   elif deit_name[4:].startswith('large'						):
      A__       =     1024
      A__       =     4096
      A__       =     24
      A__       =     16

   # load original model from timm
   A__       =     timm.create_model(UpperCAmelCase      ,pretrained=UpperCAmelCase						)
   timm_model.eval()

   # load state_dict of original model, remove and rename some keys
   A__       =     timm_model.state_dict()
   A__       =     create_rename_keys(UpperCAmelCase      ,UpperCAmelCase						)
   for src, dest in rename_keys:
      rename_key(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)
   read_in_q_k_v(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)

   # load HuggingFace model
   A__       =     DeiTForImageClassificationWithTeacher(UpperCAmelCase						).eval()
   model.load_state_dict(UpperCAmelCase						)

   # Check outputs on an image, prepared by DeiTImageProcessor
   A__       =     int(
       (256 / 224) * config.image_size						)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
   A__       =     DeiTImageProcessor(size=UpperCAmelCase      ,crop_size=config.image_size						)
   A__       =     image_processor(images=prepare_img()      ,return_tensors='pt'						)
   A__       =     encoding['pixel_values']
   A__       =     model(UpperCAmelCase						)

   A__       =     timm_model(UpperCAmelCase						)
   assert timm_logits.shape == outputs.logits.shape
   assert torch.allclose(UpperCAmelCase      ,outputs.logits      ,atol=1e-3						)

   Path(UpperCAmelCase						).mkdir(exist_ok=UpperCAmelCase						)
   print(F"""Saving model {deit_name} to {pytorch_dump_folder_path}"""						)
   model.save_pretrained(UpperCAmelCase						)
   print(F"""Saving image processor to {pytorch_dump_folder_path}"""						)
   image_processor.save_pretrained(UpperCAmelCase						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--deit_name''',
        default='''vit_deit_base_distilled_patch16_224''',
        type=str,
        help='''Name of the DeiT timm model you\'d like to convert.''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
    )

    lowerCAmelCase_     						=				parser.parse_args()
    convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)


